Ensemble learning based image classification systems

ABSTRACT

An ensemble learning based image classification system contains multiple cellular neural networks (CNN) based integrated circuits (ICs) operatively coupling together as a set of base learners of an ensemble for an image classification task. Each CNN based IC is configured with at least one distinct deep learning model in form of filter coefficients. The ensemble learning based image classification system further contains a controller configured as a meta learner of the ensemble and a memory based data buffer for holding various data used in the ensemble by the controller and the CNN based ICs. Various data may include input imagery data to be classified. Various data may also include extracted feature vectors or image classification outputs out of the set of base learners. The extracted feature vectors or image classification outputs are then used by the meta learner to further perform the image classification task.

FIELD

The patent document relates generally to the field of machine learning.More particularly, the present document relates to Ensemble learningbased image classification systems.

BACKGROUND

Cellular Neural Networks or Cellular Nonlinear Networks (CNN) have beenapplied to many different fields and problems including, but limited to,image processing since 1988. However, most of the prior art CNNapproaches are either based on software solutions (e.g., ConvolutionalNeural Networks, Recurrent Neural Networks, etc.) or based on hardwarethat are designed for other purposes (e.g., graphic processing, generalcomputation, etc.). As a result, CNN prior approaches are too slow interm of computational speed and/or too expensive thereby impractical forprocessing large amount of imagery data. Imagery data can be from anytwo-dimensional data (e.g., still photo, picture, a frame of a videostream, converted form of voice data, etc.).

Ensemble learning is a machine learning paradigm where multiple learnersare trained to solve the same problem. In contrast to ordinary machinelearning approaches which try to learn one hypothesis from trained data,ensemble methods try to construct a set of hypotheses and combine themto use. Ensemble includes a number of base learners each using adifferent hypothesis. Then a meta learner uses for combining the results(e.g., features) obtained from the base learners.

SUMMARY

This section is for the purpose of summarizing some aspects of theinvention and to briefly introduce some preferred embodiments.Simplifications or omissions in this section as well as in the abstractand the title herein may be made to avoid obscuring the purpose of thesection. Such simplifications or omissions are not intended to limit thescope of the invention.

Ensemble learning based image classification systems are described. Inone aspect of the disclosure, an ensemble learning based imageclassification system contains multiple cellular neural networks (CNN)based integrated circuits (ICs) operatively coupling together as a setof base learners of an ensemble for an image classification task. EachCNN based IC is configured with at least one distinct deep learningmodel in form of filter coefficients. The ensemble learning based imageclassification system further contains a controller configured as a metalearner of the ensemble and a memory based data buffer for holdingvarious data used in the ensemble by the controller and the CNN basedICs. Various data may include input imagery data to be classified.Various data may also include extracted feature vectors or imageclassification outputs out of the set of base learners. The extractedfeature vectors or categories are then used by the meta learner tofurther perform the image classification task. Furthermore, various datamay include classification label, which is fed directly from the baselearners to the meta learner.

In another aspect of the disclosure, an ensemble learning based imageclassification system contains a group of cellular neural networks (CNN)based integrated circuits (ICs) including one configured as a metalearner of an ensemble and remaining ones operatively coupling togetheras a set of base learners for an image classification task. Each baselearner is configured with at least one distinct deep learning model.The ensemble learning based image classification system further containsa controller for managing data flow and a memory based data buffer forholding input imagery data to be classified.

Objects, features, and advantages of the invention will become apparentupon examining the following detailed description of an embodimentthereof, taken in conjunction with the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the invention willbe better understood with regard to the following description, appendedclaims, and accompanying drawings as follows:

FIGS. 1A-1B are block diagrams illustrating an example integratedcircuit designed for extracting features from input imagery data inaccordance with one embodiment of the invention;

FIG. 2 is a function block diagram showing an example controllerconfigured for controlling operations of one or more CNN processingengines according to an embodiment of the invention;

FIG. 3 is a diagram showing an example CNN processing engine inaccordance with one embodiment of the invention;

FIG. 4 is a diagram showing M×M pixel locations within a (M+2)-pixel by(M+2)-pixel region, according to an embodiment of the invention;

FIGS. 5A-5C are diagrams showing three example pixel locations,according to an embodiment of the invention;

FIG. 6 is a diagram illustrating an example data arrangement forperforming 3×3 convolutions at a pixel location, according to oneembodiment of the invention;

FIG. 7 is a function block diagram illustrating an example circuitry forperforming 3×3 convolutions at a pixel location, according to oneembodiment of the invention;

FIG. 8 is a diagram showing an example rectification according to anembodiment of the invention;

FIGS. 9A-9B are diagrams showing two example 2×2 pooling operationsaccording to an embodiment of the invention;

FIG. 10 is a diagram illustrating a 2×2 pooling operation reducesM-pixel by M-pixel block to a (M/2)-pixel by (M/2)-pixel block inaccordance with one embodiment of the invention;

FIGS. 11A-11C are diagrams illustrating examples of M-pixel by M-pixelblocks and corresponding (M+2)-pixel by (M+2)-pixel region in an inputimage, according to one embodiment of the invention;

FIG. 12 is a diagram illustrating an example of a first set of memorybuffers for storing received imagery data in accordance with anembodiment of the invention;

FIG. 13A is a diagram showing two operational modes of an example secondset of memory buffers for storing filter coefficients in accordance withan embodiment of the invention;

FIG. 13B is a diagram showing example storage schemes of filtercoefficients in the second set of memory buffers, according to anembodiment of the invention;

FIG. 14 is a diagram showing a plurality of CNN processing enginesconnected as a loop via an example clock-skew circuit in accordance ofan embodiment of the invention;

FIG. 15 is a schematic diagram showing an example image processingtechnique based on convolutional neural networks in accordance with anembodiment of the invention;

FIG. 16 is a flowchart illustrating an example process of achieving atrained convolutional neural networks model having bi-valued 3×3 filterkernels in accordance with an embodiment of the invention;

FIG. 17 is a diagram showing an example filter kernel conversion schemein accordance with the invention;

FIG. 18 is a diagram showing an example data conversion scheme inaccordance with one embodiment of the invention;

FIG. 19 is a diagram illustrating an example ensemble learning inaccordance with one embodiment of the invention; and

FIGS. 20A-20B are function diagrams showing salient components ofexample ensemble learning based image classification systems inaccordance with one embodiment of the invention.

DETAILED DESCRIPTIONS

In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the invention. However, itwill become obvious to those skilled in the art that the invention maybe practiced without these specific details. The descriptions andrepresentations herein are the common means used by those experienced orskilled in the art to most effectively convey the substance of theirwork to others skilled in the art. In other instances, well-knownmethods, procedures, and components have not been described in detail toavoid unnecessarily obscuring aspects of the invention.

Reference herein to “one embodiment” or “an embodiment” means that aparticular feature, structure, or characteristic described in connectionwith the embodiment can be included in at least one embodiment of theinvention. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment, nor are separate or alternative embodiments mutuallyexclusive of other embodiments. Further, the order of blocks in processflowcharts or diagrams or circuits representing one or more embodimentsof the invention do not inherently indicate any particular order norimply any limitations in the invention. Used herein, the terms “upper”,“lower”, “diagonal”, “off-diagonal”, “top”, “bottom”, “right” and “left”are intended to provide relative positions for the purposes ofdescription, and are not intended to designate an absolute frame ofreference

Embodiments of the invention are discussed herein with reference toFIGS. 1A-1B-FIGS. 20A-20B. However, those skilled in the art willreadily appreciate that the detailed description given herein withrespect to these figures is for explanatory purposes as the inventionextends beyond these limited embodiments.

Referring first to FIG. 1A, it is shown a block diagram illustrating anexample digital integrated circuit (IC) 100 for extracting features outof an input image in accordance with one embodiment of the invention.

The integrated circuit 100 is implemented as a digital semi-conductorchip and contains a CNN processing engine controller 110, and one ormore cellular neural networks (CNN) processing engines 102 operativelycoupled to at least one input/output (I/O) data bus 120. Controller 110is configured to control various operations of the CNN processingengines 102 for extracting features out of an input image based on animage processing technique by performing multiple layers of 3×3convolutions with rectifications or other nonlinear operations (e.g.,sigmoid function), and 2×2 pooling operations. Performing 3×3convolutions requires imagery data in digital form and correspondingfilter coefficients, which are supplied to the CNN processing engine 102via input/output data bus 120. It is well known that digitalsemi-conductor chip contains logic gates, multiplexers, register files,memories, state machines, etc.

According to one embodiment, the digital integrated circuit 100 isextendable and scalable. For example, multiple copy of the digitalintegrated circuit 100 can be implemented on one semiconductor chip.

All of the CNN processing engines are identical. For illustrationsimplicity, only few (i.e., CNN processing engines 122 a-122 h, 132a-132 h) are shown in FIG. 1B. The invention sets no limit to the numberof CNN processing engines on a digital semi-conductor chip.

Each CNN processing engine 122 a-122 h, 132 a-132 h contains a CNNprocessing block 124, a first set of memory buffers 126 and a second setof memory buffers 128. The first set of memory buffers 126 is configuredfor receiving imagery data and for supplying the already receivedimagery data to the CNN processing block 124. The second set of memorybuffers 128 is configured for storing filter coefficients and forsupplying the already received filter coefficients to the CNN processingblock 124. In general, the number of CNN processing engines on a chip is2^(n), where n is an integer (i.e., 0, 1, 2, 3, . . . ). As shown inFIG. 1B, CNN processing engines 122 a-122 h are operatively coupled to afirst input/output data bus 130 a while CNN processing engines 132 a-132h are operatively coupled to a second input/output data bus 130 b. Eachinput/output data bus 130 a-130 b is configured for independentlytransmitting data (i.e., imagery data and filter coefficients). In oneembodiment, the first and the second sets of memory buffers compriserandom access memory (RAM). Each of the first and the second sets arelogically defined. In other words, respective sizes of the first and thesecond sets can be reconfigured to accommodate respective amounts ofimagery data and filter coefficients.

The first and the second I/O data bus 130 a-130 b are shown here toconnect the CNN processing engines 122 a-122 h, 132 a-132 h in asequential scheme. In another embodiment, the at least one I/O data busmay have different connection scheme to the CNN processing engines toaccomplish the same purpose of parallel data input and output forimproving performance.

FIG. 2 is a diagram showing an example controller 200 for controllingvarious operations of at least one CNN processing engine configured onthe integrated circuit. Controller 200 comprises circuitry to controlimagery data loading control 212, filter coefficients loading control214, imagery data output control 216, and image processing operationscontrol 218. Controller 200 further includes register files 220 forstoring the specific configuration (e.g., number of CNN processingengines, number of input/output data bus, etc.) in the integratedcircuit.

Image data loading control 212 controls loading of imagery data torespective CNN processing engines via the corresponding I/O data bus.Filter coefficients loading control 214 controls loading of filtercoefficients to respective CNN processing engines via corresponding I/Odata bus. Imagery data output control 216 controls output of the imagerydata from respective CNN processing engines via corresponding I/O databus. Image processing operations control 218 controls various operationssuch as convolutions, rectifications and pooling operations which can bedefined by user of the integrated circuit via a set of user defineddirectives (e.g., file contains a series of operations such asconvolution, rectification, pooling, etc.).

More details of a CNN processing engine 302 are shown in FIG. 3. A CNNprocessing block 304 contains digital circuitry that simultaneouslyobtains M×M convolutional operations results by performing 3×3convolutions at M×M pixel locations using imagery data of a (M+2)-pixelby (M+2)-pixel region and corresponding filter coefficients from therespective memory buffers. The (M+2)-pixel by (M+2)-pixel region isformed with the M×M pixel locations as an M-pixel by M-pixel centralportion plus a one-pixel border surrounding the central portion. M is apositive integer. In one embodiment, M equals to 14 and therefore, (M+2)equals to 16, M×M equals to 14×14=196, and M/2 equals 7.

FIG. 4 is a diagram showing a diagram representing (M+2)-pixel by(M+2)-pixel region 410 with a central portion of M×M pixel locations 420used in the CNN processing engine 302.

Imagery data may represent characteristics of a pixel in the input image(e.g., one of the color (e.g., RGB (red, green, blue)) values of thepixel, or distance between pixel and observing location). Generally, thevalue of the RGB is an integer between 0 and 255. Values of filtercoefficients are floating point integer numbers that can be eitherpositive or negative.

In order to achieve faster computations, few computational performanceimprovement techniques have been used and implemented in the CNNprocessing block 304. In one embodiment, representation of imagery datauses as few bits as practical (e.g., 5-bit representation). In anotherembodiment, each filter coefficient is represented as an integer with aradix point. Similarly, the integer representing the filter coefficientuses as few bits as practical (e.g., 12-bit representation). As aresult, 3×3 convolutions can then be performed using fixed-pointarithmetic for faster computations.

Each 3×3 convolution produces one convolutional operations result,Out(m, n), based on the following formula:

$\begin{matrix}{{{Out}\left( {m,n} \right)} = {{\sum\limits_{{1 \leq i},{j \leq 3}}{{{In}\left( {m,n,i,j} \right)} \times {C\left( {i,j} \right)}}} - b}} & (1)\end{matrix}$where:

-   -   m, n are corresponding row and column numbers for identifying        which imagery data (pixel) within the (M+2)-pixel by (M+2)-pixel        region the convolution is performed;    -   In(m,n,i,j) is a 3-pixel by 3-pixel area centered at pixel        location (m, n) within the region;    -   C(i, j) represents one of the nine weight coefficients C(3×3),        each corresponds to one of the 3-pixel by 3-pixel area;    -   b represents an offset coefficient; and    -   i, j are indices of weight coefficients C(i, j).

Each CNN processing block 304 produces M×M convolutional operationsresults simultaneously and, all CNN processing engines performsimultaneous operations.

FIGS. 5A-5C show three different examples of the M×M pixel locations.The first pixel location 531 shown in FIG. 5A is in the center of a3-pixel by 3-pixel area within the (M+2)-pixel by (M+2)-pixel region atthe upper left corner. The second pixel location 532 shown in FIG. 5B isone pixel data shift to the right of the first pixel location 531. Thethird pixel location 533 shown in FIG. 5C is a typical example pixellocation. M×M pixel locations contains multiple overlapping 3-pixel by3-pixel areas within the (M+2)-pixel by (M+2)-pixel region.

To perform 3×3 convolutions at each sampling location, an example dataarrangement is shown in FIG. 6. Imagery data (i.e., In(3×3)) and filtercoefficients (i.e., weight coefficients C(3×3) and an offset coefficientb) are fed into an example CNN 3×3 circuitry 600. After 3×3 convolutionsoperation in accordance with Formula (1), one output result (i.e.,Out(1×1)) is produced. At each sampling location, the imagery dataIn(3×3) is centered at pixel coordinates (m, n) 605 with eight immediateneighbor pixels 601-604, 606-609.

FIG. 7 is a function diagram showing an example CNN 3×3 circuitry 700for performing 3×3 convolutions at each pixel location. The circuitry700 contains at least adder 721, multiplier 722, shifter 723, rectifier724 and pooling operator 725. In a digital semi-conductorimplementation, all of these can be achieved with logic gates andmultiplexers, which are generated using well-known methods (e.g.,hardware description language such as Verilog, etc.). Adder 721 andmultiplier 722 are used for addition and multiplication operations.Shifter 723 is for shifting the output result in accordance withfixed-point arithmetic involved in the 3×3 convolutions. Rectifier 724is for setting negative output results to zero. Pooling operator 725 isfor performing 2×2 pooling operations.

Imagery data are stored in a first set of memory buffers 306, whilefilter coefficients are stored in a second set of memory buffers 308.Both imagery data and filter coefficients are fed to the CNN block 304at each clock of the digital integrated circuit. Filter coefficients(i.e., C(3×3) and b) are fed into the CNN processing block 304 directlyfrom the second set of memory buffers 308. However, imagery data are fedinto the CNN processing block 304 via a multiplexer MUX 305 from thefirst set of memory buffers 306. Multiplexer 305 selects imagery datafrom the first set of memory buffers based on a clock signal (e.g.,pulse 312).

Otherwise, multiplexer MUX 305 selects imagery data from a firstneighbor CNN processing engine (from the left side of FIG. 3 not shown)through a clock-skew circuit 320.

At the same time, a copy of the imagery data fed into the CNN processingblock 304 is sent to a second neighbor CNN processing engine (to theright side of FIG. 3 not shown) via the clock-skew circuit 320.Clock-skew circuit 320 can be achieved with known techniques (e.g., a Dflip-flop 322).

The first neighbor CNN processing engine may be referred to as anupstream neighbor CNN processing engine in the loop formed by theclock-skew circuit 320. The second neighbor CNN processing engine may bereferred to as a downstream CNN processing engine. In anotherembodiment, when the data flow direction of the clock-skew circuit isreversed, the first and the second CNN processing engines are alsoreversed becoming downstream and upstream neighbors, respectively.

After 3×3 convolutions for each group of imagery data are performed forpredefined number of filter coefficients, convolutional operationsresults Out(m, n) are sent to the first set of memory buffers viaanother multiplex MUX 307 based on another clock signal (e.g., pulse311). An example clock cycle 310 is drawn for demonstrating the timerelationship between pulse 311 and pulse 312. As shown pulse 311 is oneclock before pulse 312, results of the 3×3 convolutional operations arestored into the first set of memory buffers after a particular block ofimagery data has been processed by all CNN processing engines throughthe clock-skew circuit 320.

After the convolutional operations result Out(m, n) is obtained fromFormula (1), rectification procedure may be performed as directed byimage processing control 218. Any convolutional operations result,Out(m, n), less than zero (i.e., negative value) is set to zero. Inother words, only positive value of output results are kept. FIG. 8shows two example outcomes of rectification. A positive output value10.5 retains as 10.5 while −2.3 becomes 0. Rectification causesnon-linearity in the integrated circuits.

If a 2×2 pooling operation is required, the M×M output results arereduced to (M/2)×(M/2). In order to store the (M/2)×(M/2) output resultsin corresponding locations in the first set of memory buffers,additional bookkeeping techniques are required to track proper memoryaddresses such that four (M/2)×(M/2) output results can be processed inone CNN processing engine.

To demonstrate a 2×2 pooling operation, FIG. 9A is a diagram graphicallyshowing first example output results of a 2-pixel by 2-pixel block beingreduced to a single value 10.5, which is the largest value of the fouroutput results. The technique shown in FIG. 9A is referred to as “maxpooling”. When the average value 4.6 of the four output results is usedfor the single value shown in FIG. 9B, it is referred to as “averagepooling”. There are other pooling operations, for example, “mixed maxaverage pooling” which is a combination of “max pooling” and “averagepooling”. The main goal of the pooling operation is to reduce the sizeof the imagery data being processed. FIG. 10 is a diagram illustratingM×M pixel locations, through a 2×2 pooling operation, being reduced to(M/2)×(M/2) locations, which is one fourth of the original size.

An input image generally contains a large amount of imagery data. Inorder to perform image processing operations. The input image 1100 ispartitioned into M-pixel by M-pixel blocks 1111-1112 as shown in FIG.11A. Imagery data associated with each of these M-pixel by M-pixelblocks is then fed into respective CNN processing engines. At each ofthe M×M pixel locations in a particular M-pixel by M-pixel block, 3×3convolutions are simultaneously performed in the corresponding CNNprocessing block.

Although the invention does not require specific characteristicdimension of an input image, the input image may be required to resizeto fit into a predefined characteristic dimension for certain imageprocessing procedures. In an embodiment, a square shape with(2^(K)×M)-pixel by (2^(K)×M)-pixel is required. K is a positive integer(e.g., 1, 2, 3, 4, etc.). When M equals 14 and K equals 4, thecharacteristic dimension is 224. In another embodiment, the input imageis a rectangular shape with dimensions of (2^(I)×M)-pixel and(2^(J)×M)-pixel, where I and J are positive integers.

In order to properly perform 3×3 convolutions at pixel locations aroundthe border of a M-pixel by M-pixel block, additional imagery data fromneighboring blocks are required. FIG. 11B shows a typical M-pixel byM-pixel block 1120 (bordered with dotted lines) within a (M+2)-pixel by(M+2)-pixel region 1130. The (M+2)-pixel by (M+2)-pixel region is formedby a central portion of M-pixel by M-pixel from the current block, andfour edges (i.e., top, right, bottom and left) and four corners (i.e.,top-left, top-right, bottom-right and bottom-left) from correspondingneighboring blocks. Additional details are shown in FIG. 12 andcorresponding descriptions for the first set of memory buffers.

FIG. 11C shows two example M-pixel by M-pixel blocks 1122-1124 andrespective associated (M+2)-pixel by (M+2)-pixel regions 1132-1134.These two example blocks 1122-1124 are located along the perimeter ofthe input image. The first example M-pixel by M-pixel block 1122 islocated at top-left corner, therefore, the first example block 1122 hasneighbors for two edges and one corner. Value “0”s are used for the twoedges and three corners without neighbors (shown as shaded area) in theassociated (M+2)-pixel by (M+2)-pixel region 1132 for forming imagerydata. Similarly, the associated (M+2)-pixel by (M+2)-pixel region 1134of the second example block 1124 requires “0”s be used for the top edgeand two top corners. Other blocks along the perimeter of the input imageare treated similarly. In other words, for the purpose to perform 3×3convolutions at each pixel of the input image, a layer of zeros (“0”s)is added outside of the perimeter of the input image. This can beachieved with many well-known techniques. For example, default values ofthe first set of memory buffers are set to zero. If no imagery data isfilled in from the neighboring blocks, those edges and corners wouldcontain zeros.

Furthermore, an input image can contain a large amount of imagery data,which may not be able to be fed into the CNN processing engines in itsentirety. Therefore, the first set of memory buffers is configured onthe respective CNN processing engines for storing a portion of theimagery data of the input image. The first set of memory bufferscontains nine different data buffers graphically illustrated in FIG. 12.Nine buffers are designed to match the (M+2)-pixel by (M+2)-pixel regionas follows:

1) buffer-0 for storing M×M pixels of imagery data representing thecentral portion;

2) buffer-1 for storing 1×M pixels of imagery data representing the topedge;

3) buffer-2 for storing M×1 pixels of imagery data representing theright edge;

4) buffer-3 for storing 1×M pixels of imagery data representing thebottom edge;

5) buffer-4 for storing M×1 pixels of imagery data representing the leftedge;

6) buffer-5 for storing 1×1 pixels of imagery data representing the topleft corner;

7) buffer-6 for storing 1×1 pixels of imagery data representing the topright corner;

8) buffer-7 for storing 1×1 pixels of imagery data representing thebottom right corner; and

9) buffer-8 for storing 1×1 pixels of imagery data representing thebottom left corner.

Imagery data received from the I/O data bus are in form of M×M pixels ofimagery data in consecutive blocks. Each M×M pixels of imagery data isstored into buffer-0 of the current block. The left column of thereceived M×M pixels of imagery data is stored into buffer-2 of previousblock, while the right column of the received M×M pixels of imagery datais stored into buffer-4 of next block. The top and the bottom rows andfour corners of the received M×M pixels of imagery data are stored intorespective buffers of corresponding blocks based on the geometry of theinput image (e.g., FIGS. 11A-11C).

An example second set of memory buffers for storing filter coefficientsare shown in FIG. 13A. In one embodiment, a pair of independent buffersBuffer0 1301 and Buffer1 1302 is provided. The pair of independentbuffers allow one of the buffers 1301-1302 to receive data from the I/Odata bus 1330 while the other one to feed data into a CNN processingblock (not shown). Two operational modes are shown herein.

Example storage schemes of filter coefficients are shown in FIG. 13B.Each of the pair of buffers (i.e., Buffer0 1301 or Buffer1 1302) has awidth (i.e., word size 1310). In one embodiment, the word size is120-bit. Accordingly, each of the filter coefficients (i.e., C(3×3) andb) occupies 12-bit in the first example storage scheme 1311. In thesecond example storage scheme 1312, each filter coefficient occupies6-bit thereby 20 coefficients are stored in each word. In the thirdexample scheme 1313, 3-bit is used for each coefficient hence four setsof filter coefficients (40 coefficients) are stored. Finally, in thefourth example storage scheme 1314, 80 coefficients are stored in eachword, each coefficient occupies 1.5-bit.

In another embodiment, a third memory buffer can be set up for storingentire filter coefficients to avoid I/O delay. In general, the inputimage must be at certain size such that all filter coefficients can bestored. This can be done by allocating some unused capacity in the firstset of memory buffers to accommodate such a third memory buffer. Sinceall memory buffers are logically defined in RAM (Random-Access Memory),well known techniques may be used for creating the third memory buffer.In other words, the first and the second sets of memory buffers can beadjusted to fit different amounts of imagery data and/or filtercoefficients. Furthermore, the total amount of RAM is dependent uponwhat is required in image processing operations.

When more than one CNN processing engine is configured on the integratedcircuit. The CNN processing engine is connected to first and secondneighbor CNN processing engines via a clock-skew circuit. Forillustration simplicity, only CNN processing block and memory buffersfor imagery data are shown. An example clock-skew circuit 1440 for agroup of CNN processing engines are shown in FIG. 14. The CNN processingengines connected via the second example clock-skew circuit 1440 to forma loop. In other words, each CNN processing engine sends its own imagerydata to a first neighbor and, at the same time, receives a secondneighbor's imagery data. Clock-skew circuit 1440 can be achieved withwell-known manners. For example, each CNN processing engine is connectedwith a D flip-flop 1442.

A special case with only two CNN processing engines are connected in aloop, the first neighbor and the second neighbor are the same.

Referring now to FIG. 15, it is a schematic diagram showing an exampleimage processing technique based on convolutional neural networks inaccordance with an embodiment of the invention. Based on convolutionalneural networks, multi-layer input imagery data 1511 a-1511 c isprocessed with convolutions using a first set of filters or weights1520. Since the imagery data 1511 a-1511 c is larger than the filters1520. Each corresponding overlapped sub-region 1515 of the imagery datais processed. After the convolutional results are obtained, activationmay be conducted before a first pooling operation 1530. In oneembodiment, activation is achieved with rectification performed in arectified linear unit (ReLU). As a result of the first pooling operation1530, the imagery data is reduced to a reduced set of imagery data 1531a-1531 c. For 2×2 pooling, the reduced set of imagery data is reduced bya factor of 4 from the previous set.

The previous convolution-to-pooling procedure is repeated. The reducedset of imagery data 1531 a-1531 c is then processed with convolutionsusing a second set of filters 1540. Similarly, each overlappedsub-region 1535 is processed. Another activation may be conducted beforea second pooling operation 1540. The convolution-to-pooling proceduresare repeated for several layers and finally connected to at least oneFully-connected (FC) layer 1560. In image classification, respectiveprobabilities of predefined categories can be computed in FC layers1560.

This repeated convolution-to-pooling procedure is trained using a knowndataset or database. For image classification, the dataset contains thepredefined categories. A particular set of filters, activation andpooling can be tuned and obtained before use for classifying an imagerydata, for example, a specific combination of filter types, number offilters, order of filters, pooling types, and/or when to performactivation. In one embodiment, convolutional neural networks are basedon Visual Geometry Group (VGG16) architecture neural nets, whichcontains 13 convolutional layers and three fully-connected layers.

A trained convolutional neural networks model is achieved with anexample set of operations 1600 shown in FIG. 16. At action 1602, aconvolutional neural networks model is first obtained by training theconvolutional neural networks model based on image classification of alabeled dataset, which contains a sufficiently large number of inputdata (e.g., imagery data, converted voice data, optical characterreorganization (OCR) data, etc.). For example, there are at least 4,000data for each category. In other words, each data in the labeled datasetis associated with a category to be classified. The convolutional neuralnetworks model includes multiple ordered filter groups (e.g., eachfilter group corresponds to a convolutional layer in the convolutionalneural networks model). Each filter in the multiple ordered filtergroups contains a standard 3×3 filter kernel (i.e., nine coefficients infloating point number format (e.g., standard 3×3 filter kernel 1710 inFIG. 17)). Each of the nine coefficients can be any negative or positivereal number (i.e., a number with fraction). The initial convolutionalneural networks model may be obtained from many different frameworksincluding, but not limited to, Mxnet, caffe, tensorflow, etc.

Then, at action 1604, the convolutional neural networks model ismodified by converting respective standard 3×3 filter kernels 1710 tocorresponding bi-valued 3×3 filter kernels 1720 of a currently-processedfilter group in the multiple ordered filter groups based on a set ofkernel conversion schemes. In one embodiment, each of the ninecoefficients C(i,j) in the corresponding bi-valued 3×3 filter kernel1720 is assigned a value ‘A’ which equals to the average of absolutecoefficient values multiplied by the sign of corresponding coefficientsin the standard 3×3 filter kernel 1710 shown in following formula:

$\begin{matrix}{A = {\sum\limits_{{1 \leq i},{j \leq 3}}{{{C\left( {i,j} \right)}}/9}}} & (2)\end{matrix}$

Filter groups are converted one at a time in the order defined in themultiple ordered filter groups. In certain situation, two consecutivefilter groups are optionally combined such that the training of theconvolutional neural networks model is more efficient.

Next, at action 1606, the modified convolutional neural networks modelis retrained until a desired convergence criterion is met or achieved.There are a number of well known convergence criteria including, but notlimited to, completing a predefined number of retraining operation,converging of accuracy loss due to filter kernel conversion, etc. In oneembodiment, all filter groups including those already converted inprevious retraining operations can be changed or altered for finetuning. In another embodiment, the already converted filter groups arefrozen or unaltered during the retraining operation of thecurrently-processed filter group.

Process 1600 moves to decision 1608, it is determined whether there isanother unconverted filter group. If ‘yes’, process 1600 moves back torepeat actions 1604-1606 until all filter groups have been converted.Decision 1608 becomes ‘no’ thereafter. At action 1610, coefficients ofbi-valued 3×3 filter kernels in all filter groups are transformed from afloating point number format to a fixed point number format toaccommodate the data structure required in the CNN based integratedcircuit. Furthermore, the fixed point number is implemented asreconfigurable circuits in the CNN based integrated circuit. In oneembodiment, the coefficients are implemented using 12-bit fixed pointnumber format.

FIG. 18 is a diagram showing an example data conversion scheme forconverting data from 8-bit [0-255] to 5-bit [0-31] per pixel. Forexample, bits 0-7 becomes 0, bits 8-15 becomes 1, etc.

As described in process 1600 of FIG. 16, a convolutional neural networksmodel is trained for the CNN based integrated circuit. The entire set oftrained coefficients or weights are pre-configured to the CNN basedintegrated circuit as a feature extractor for a particular data format(e.g., imagery data, voice spectrum, fingerprint, palm-print, opticalcharacter recognition, etc.). In general, there are many convolutionallayers with many filters in each layer. In one embodiment, VGG16 modelcontains 13 convolutional layers. In a software based imageclassification task, computations for the convolutional layers takemajority of computations (e.g., 90%) traditionally. This computations isdrastically reduced with a dedicated hardware such as CNN based IC 100.

In another embodiment, entire set of coefficients can be trained on theCNN based integrated circuit. In other words, the conversion from fullfloating point number format to fixed point number format is notnecessary. The coefficients of bi-valued 3×3 filter kernels are traineddirectly. Conversion from standard kernel to bi-value filter kernel isnot required.

In one embodiment, fixed point number format is adjustable to fitdifferent requirements (e.g., range of filter coefficients). In otherwords, number of digits after the radix point may be adjusted.

An example ensemble learning method is shown in FIG. 19. Exact copy ofinput imagery data 1910 is processed through each of a set of baselearners 1930, which is configured with distinct deep learning models(i.e., Model-1 1920 a, Model-2 1920 b, . . . , Model-t, 1920 t). Thedistinct deep learning models are trained using a common labeleddatabase for image classification of a predefined set of categories.Deep learning models may include, but are not limited to, VGG16 modelwith bi-value filter coefficients, using reduced number of channels forextracting features, etc. There are many other techniques for creatingthe distinct deep learning models. Diversified models are achieved byusing different portions of the common labeled database for training.Diversity may also be achieved with one particular model using differenttraining techniques including, but not limited to, differentinitializations, different epochs, different learning rates, etc.

Extracted feature vectors or image classification outputs are outputresults out of the base learners 1930. Examples of image classificationoutputs may include, but are not limited to, top 1 prediction, top 5prediction, etc. Top 5 prediction is the top five categories withhighest classification probability score. Meta learner 1940 then usesthe output results of the base learners 1930 as input to obtain acategory in the image classification task. In one embodiment, the metalearner 1940 is configured with machine learning model (e.g., weightedvoting, support vector machine, logistic regression, random forests,gradient boosting decision trees, etc.). Techniques used in an ensemblemay include, but are not limited to, blending, stacking, boosting,bagging, etc.

Imagery data 1910 can be two-dimensional data in form of pixels (e.g.,224×224×3 three basic colors of 224×224 pixels).

FIG. 20A shows salient components of a first example ensemble learningbased image classification system 2020. The first example system 2020contains a group of CNN based ICs 2022 a-2022 r as base learners, acontroller 2023 as a meta learner and a memory based data buffer 2024for holding various data used in the ensemble.

Each of the CNN based ICs 2022 a-2022 r is configured with at least onedistinct deep learning model in form of filter coefficients. In oneembodiment, each CNN based IC is loaded with pre-trained filtercoefficients of one unique deep learning model. In another embodiment,CNN based IC is figured with more than one deep learning models, if thetotal amount of pre-trained filter coefficients can be stored within thememory space within the CNN based IC.

When magnetic random access memory is implemented in the CNN based IC,it generally provides larger memory space comparing to static randomaccess memory implemented.

Controller 2023 is configured as a meta learner using machine learningmodel. Memory based data buffer 2024 is configured for holding variousdata in the ensemble, for example, relevant portion of input imagerydata before loading into the CNN based ICs 2022 a-2022 r, extractedfeature vectors, coefficients of fully-connected (FC) layers, etc. Thecontroller 2023 may also be configured for performing computations of FClayers for those deep learning models requiring FC layers. In oneembodiment, memory based data buffer 2024 is implemented using staticrandom access memory.

Salient components of a second example ensemble learning based imageclassification system 2040 are shown in FIG. 20B. The second examplesystem 2040 contains a group of CNN based ICs 2042 a-2042 s with one ofthe group (e.g., CNN based IC 2042 a) configured as a meta learner andthe remaining ones (e.g., CNN based ICs 2042 b-2042 s) configures as aset of base learners. The second example ensemble learning based imageclassification system 2040 further contains a controller 2043 and amemory based data buffer 2044. Similar to the first example system 2020,each of the base learners is loaded with pre-trained filter coefficientsof at least one distinct deep learning model. Meta learner is based onanother deep learning model, which uses concatenated feature vectors outof the base learners as input to further performing the imageclassification task. In one embodiment, padding values (e.g., Os) areused for unfilled locations of concatenated feature vectors.

A number of physical implementations of the ensemble learning basedimage classification system may be used. For example, the first exampleensemble learning based image classification system 2020 may beimplemented by mounting all components (i.e., CNN based ICs 2022 a-2022r), controller 2023 and memory based buffer 2024 on a printed circuitboard (PCB). In another embodiment, one or more CNN based ICs may bepackaged in a dongle. Multiple dongles may be connected with one anothervia a hub e.g., Universal Serial Bus (USB) hub.

Although the invention has been described with reference to specificembodiments thereof, these embodiments are merely illustrative, and notrestrictive of, the invention. Various modifications or changes to thespecifically disclosed example embodiments will be suggested to personsskilled in the art. For example, whereas the input image has been shownand described as partitioning into M-pixel by M-pixel blocks in certainorder, other orders may be used in the invention to achieve the same,for example, the ordering of the M-pixel by M-pixel blocks may becolumn-wise instead of row-wise. Furthermore, whereas M-pixel by M-pixelblocks have been shown and described using M equals to 14 as an example.M can be chosen as other positive integers to accomplish the same, forexample, 16, 20, 30, etc. Finally, whereas few example machine learningmodels have been described in meta learner, other types of machinelearning models may be used for achieving the same. In summary, thescope of the invention should not be restricted to the specific exampleembodiments disclosed herein, and all modifications that are readilysuggested to those of ordinary skill in the art should be includedwithin the spirit and purview of this application and scope of theappended claims.

What is claimed is:
 1. An ensemble learning based image classificationsystem comprising: a plurality of cellular neural networks (CNN) basedintegrated circuits (ICs) operatively coupling together as a set of baselearners of an ensemble for an image classification task, each CNN basedIC being configured with at least one of a plurality of distinct deeplearning models; a controller configured as a meta learner of theensemble; and a memory based data buffer for holding various data usedin the ensemble by the controller and the plurality of CNN based ICs. 2.The ensemble learning based image classification system of claim 1,wherein the plurality of CNN based ICs is coupled together via a printedcircuit board.
 3. The ensemble learning based image classificationsystem of claim 1, wherein the plurality of CNN based ICs is coupledtogether via a hub of dongles.
 4. The ensemble learning based imageclassification system of claim 1, wherein all of the distinct deeplearning models being trained using a common labeled database for a setof categories used in the image classification task.
 5. The ensemblelearning based image classification system of claim 1, wherein thevarious data comprises input imagery data to be classified.
 6. Theensemble learning based image classification system of claim 5, whereinsaid each CNN based IC further comprising: a plurality of cellularneural networks (CNN) processing engines operatively coupled to at leastone input/output data bus, the plurality of CNN processing engines beingconnected in a loop with a clock-skew circuit, each CNN processingengine comprising: a CNN processing block configured for simultaneouslyperforming convolutional operations using the input imagery data andpre-trained filter coefficients of the at least one of the plurality ofdistinct deep learning models; a first set of memory buffers operativelycoupling to the CNN processing block for storing relevant portion of theinput imagery data; and a second set of memory buffers operativecoupling to the CNN processing block for storing the pre-trained filtercoefficients.
 7. The ensemble learning based image classification systemof claim 1, wherein the various data comprises extracted feature vectorsout of the set of base learners.
 8. The ensemble learning based imageclassification system of claim 7, wherein the extracted feature vectorsare used by the meta learner to further perform the image classificationtask.
 9. The ensemble learning based image classification system ofclaim 8, wherein the meta learner is configured with a machine learningmodel selected from the group consisting of (i) weighted voting, (ii)support vector machine, (iii) logistic regression, (iv) random forests,and (v) gradient boosting decision trees.
 10. The ensemble learningbased image classification system of claim 1, wherein the various datacomprises image classification outputs out of the set of base learners.11. The ensemble learning based image classification system of claim 10,wherein the image classification outputs are used by the meta learner tofurther perform the image classification task.
 12. The ensemble learningbased image classification system of claim 8, wherein the meta learneris configured with a machine learning model selected from the groupconsisting of (i) weighted voting, (ii) support vector machine, (iii)logistic regression, (iv) random forests, and (v) gradient boostingdecision trees.
 13. The ensemble learning based image classificationsystem of claim 1, wherein the controller is further configured forperforming computations of fully-connected layers.